Hi,
I am having trouble using the lme function to perform a nested ANOVA
with a random nested factor.
My design is as follows:
Location (n=6) (Random)
Site nested within each Location (n=12) (2 Sites nested within each
Location) (Random)
Dependent variable: sp (species abundance)
By using the aov function I can generate a nested ANOVA, however this
assumes that my nested factor is fixed.
> summary(aov(sp~Location/Site, data=mavric))
Df Sum Sq Mean Sq F value Pr(>F)
Location 4 112366 28092 1.2742 0.2962
Location:Transect 5 121690 24338 1.1039 0.3736
Residuals 40 881875 22047
I have tried the following lme function to specify that Site is random:
> lme1 <- lme(sp~Location, random=~1|Site, data=mavric)
> lme2 <- lme(sp~Location, random=~1|Location/Site, data=mavric)
> anova(lme1)
numDF denDF F-value p-value
(Intercept) 1 40 3.418077 0.0719
Location 4 5 1.152505 0.4294
This gives me the correct F-value for Location from
MSLocation/MSLocation:Transect, but the p-value doesn't seem to be
correct (by my calculations in Microsoft Excel it should be 0.345)
> anova(lme2)
Warning in pf(q, df1, df2, lower.tail, log.p) :
NaNs produced
Warning: NAs introduced by coercion
numDF denDF F-value p-value
(Intercept) 1 40 0.459966 0.5015
Location 4 0 0.155091 NaN
? I don't know what this output means
> anova(lme1,lme2)
Model df AIC BIC logLik Test L.Ratio p-value
lme1 1 7 603.7534 616.4000 -294.8767
lme2 2 8 605.7534 620.2067 -294.8767 1 vs 2 1.815674e-05 0.9966
? I also don't know what this output means.
Can anyone tell me if there is a way to use the lme() function in order
to obtain the same output as the aov() function (above), but so it
correctly calculates the MS, F and p values for my main Location factor?
Thanks,
Prue
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